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Principles, Models, and Methods for the Characterization and Analysis of Lurkers in Online Social Networks

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Principles, Models, and Methods for the Characterization and Analysis of Lurkers in Online Social Networks Principles, models, and methods for the characterization and analysis of lurkers in online social networks Roberto Interdonato, Andrea Tagarelli DIMES Dept., Università della Calabria, Italy The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Lurking in OSNs: Principles, Models, and Methods Lurking in OSNs: Principles, Models, and Methods Lurk(er): what meanings Lurking in OSNs: Principles, Models, and Methods “Lurker”: let’s google it … Lurking in OSNs: Principles, Models, and Methods Lurk(er): what meanings Lurking in OSNs: Principles, Models, and Methods Outline 1. Lurking in online communities 2. Modeling lurking behaviors Topology-driven lurking definition The issue of controversial definitions Lurking and online behavioral 3. Lurker ranking methods models 4. Experimental evaluation The opportunity of de-lurking Static scenarios Dynamic scenarios 5. Applications to other domains Vicariously learning Lurking in social trust contexts 6. Delurking via Targeted Influence Maximization The DEvOTION algorithm 7. Conclusion and future work Lurking in OSNs: Principles, Models, and Methods LURKING IN ONLINE COMMUNITIES Lurking in OSNs: Principles, Models, and Methods The 1:9:90 rule of participation inequality (1/3) Arthur, C. (2006). What is the 1% rule? In: The guardian. UK: Guardian News and Media. Lurking in OSNs: Principles, Models, and Methods The 1:9:90 rule of participation inequality (2/3) • [Nonnecke & Preece, 2000] Email-based discussion lists: • 77 online health support groups and 21 online technical support groups • 46% of the health support group members and 82% of the technical support group members are lurkers • [Swartz, 2006] On Wikipedia: over 50% of all the edits are done by only 0.7% of the users • [van Mierlo, 2014] On four DHSNs (AlcoholHelpCenter, DepressionCenter, PanicCenter, and StopSmokingCenter): • 63,990 users, 578,349 posts • Lurkers account for 1.3% (n=4668), Contributors for 24.0% (n=88,732), and Superusers for 74.7% (n=276,034) of content Nonnecke, B., Preece, J. (2000). Lurker Demographics: Counting the Silent. In Proc. SIGCHI Human Factors in Computing. Swartz, A. (2006). Raw thought: Who writes Wikipedia. Blog article at www.aaronsw.com/weblog/whowriteswikipedia. van Mierlo, T. (2014). The 1% rule in four digital health social networks: An observational study. Medical Internet Research, 16(2). Lurking in OSNs: Principles, Models, and Methods The 1:9:90 rule of participation inequality (3/3) • Online learning courses: • No relation between interactivity (i.e., posting) and learning (i.e., earned grade) • Extend the notion of interactivity to include the lurking activity • Each of the 128 students reads at least one contribution • 62% of the class are lurkers—only reading posts, not contributing anything • No correlation between the no. of readers and the no. of writers • Every participant, active or lurking, reads more postings than they write • Active participation in an online discussion list, based on passive lurking, is expressed by reading, reflecting on the contribution of all the other members Ebner, M., Holzinger, A. (2005). Lurking: An underestimated human-computer phenomenon. IEEE Multimedia, 12(4), 70–75. Lurking in OSNs: Principles, Models, and Methods Perception of lurking (1/2) • Lurkers as “free-riders” [Kollock & Smith,1996; Morris & Ogan, 1996; Wellman & Gulia,1999; Rheingold, 2000] • Sustainability of an online community • Fresh content and timely interactions • Lurkers contribute little value [van Mierlo, 2014] • Lurkers may impair the virality of the community [Nielsen, 2011] Kollock, P., Smith, M. (1996). Managing the virtual commons. Computer-mediated communication: Linguistic, social, and cross- cultural perspectives, 109–128. Morris, M., Ogan, C. (1996). The internet as mass medium. Journal of Communication, 46(1), 39–50. Wellman, B., Gulia, M. (1999). Net surfers don’t ride alone: Virtual communities as communities. Networks in the Global Village, 331– 366. Rheingold, H. (2000). The virtual community: Homesteading on the electronic frontier. MIT Press. Nielsen, J. (2011). Participation inequality: Encouraging more users to contribute, http://www.useit.com/alertbox/ participation_inequality.html. Lurking in OSNs: Principles, Models, and Methods Perception of lurking (2/2) • Most lurkers are NOT free-riders (e.g., [Nonnecke, Preece, & Andrews, 2004; Nonnecke, Andrews, & Preece, 2006]) • Lurking can be regarded as passive participation that permits inclusion [Ferree, 2002] • Lurking is normal and an active, participative and valuable form of online behavior [Edelmann, 2013] • Lurkers perceive themselves as community members [Nonnecke et al., 2006] • Lurking as a form of cognitive apprenticeship: “legitimate peripheral participation” [Lave & Wenger, 1999] Nonnecke, B., Preece, J., Andrews, D. (2004). What lurkers and posters think of each other. In Proc. the 37th annual Hawaii Int. Conf. on System Sciences. Nonnecke, B., Andrews, D., Preece, J. (2006). Non-public and public online community participation: Needs, attitudes and behavior. Electronic Commerce Research, 6(1), 7–20. Ferree, M. M., Gamson, W. A., Gerhards, J., Rucht, D. (2002). Shaping abortion discourse: Democracy and the public sphere in Germany and the United States. New York, Cambridge University Press. Edelmann, N. (2013). Reviewing the definitions of ‘‘Lurkers’’ and some implications for online research. Cyberpsychology, Behavior, and Social Networking, 16(9), 645–649. Lave, J., Wenger, E. (1999). Legitimate peripheral participation. Learners, learning and assessment. London: The Open University, pp. 83–89. Lurking in OSNs: Principles, Models, and Methods How to identify lurkers (1/4) • Two main features: seldom posting, mostly reading contents • Attempts to set quantitative standards: • “never post in an online community” [Nonnecke et al., 2006] • “post messages only once in a long while” [Golder & Donath, 2004] • “no contribution during a 3-month period” [Nonnecke & Preece, 2000] • “#posts<4 from the beginning, or never posted in the last 4 months” [Ganley et al., 2012] • Accounting for the “login” dimension [Chen, 2004] • Lurkers log into the community every week throughout a 6-week timespan Golder, S. A., Donath, J. (2004). Social roles in electronic communities. Internet Research, 5, 19–22. Ganley, D., Moser, C., Groenewegen, P. (2012). Categorizing behavior in online communities: A look into the world of cake bakers. In Proc. HICSS, pp. 3457–3466. Chen, F. C. (2004). Passive forum behaviors (lurking): A community perspective. In Proc. 6th Int. Conf. on Learning Sciences, pp. 128–135. Lurking in OSNs: Principles, Models, and Methods How to identify lurkers (2/4) • Find a certain percentage of most non-active users as lurkers • e.g., [Rau et al., 2008] On Microsoft’s Wallop SNS, 40% of the most non-active as lurkers • Two continuous dimensions (participation pattern) [Leshed, 2005]: • Publicity: ratio of public (i.e., posting) to non-public (i.e., reading) activities • Intensity: the frequency of total activities performed by a member • Lurkers tend to have higher intensity and lower publicity Rau, P.-L. P., Gao, Q., Ding, Y. (2008). Relationship between the level of intimacy and lurking in online social network services. Computers in Human Behavior, 24(6), 2757–2770. Leshed, G. (2005). Posters, lurkers, and in between: A multidimensional model of online community participation patterns. In Proc. HIC. Lurking in OSNs: Principles, Models, and Methods How to identify lurkers (3/4) • Lurkers may be classified into: [Takahashi et al. 2003; Walker et al. 2013] • Passive lurkers: only read for their use • Active lurkers: for propagation, practical use, or personal contact • Lurkers vs. “non-users” [Springer et al. 2015] • Lurking as passive participation, as opposed to commenting (active participation) • Non-users: read news but have no interest in the user comments/discussions Takahashi, M., Fujimoto, M., Yamasaki, N. (2003). The active lurker: Influence of an in-house online community on its outside environment. In Proc. ACM SIGGROUP Conf. on Supporting Group Work, pp. 1–10. Walker, B., Redmond, J., Lengyel, A. (2013). Are they all the same? Lurkers and posters on the net. eCULTURE, 3(1). Springer, N., Engelmann, I., Pfaffinger, C. (2015). User comments: motives and inhibitors to write and read. Information, Communication & Society, 18(7): 798-815 Lurking in OSNs: Principles, Models, and Methods How to identify lurkers (4/4) • Can we generalize using the previously discussed criteria? • No, it depends on the size, topics and culture of the online community! • Many factors influence online behaviors (e.g., [Bishop, 2007; Fan et al., 2009]): • Environmental influences • Personal characteristics • Organizational commitment • Many lurkers: good or bad? • Active lurkers are beneficial for the propaganda and development of the community • but they have low posting rate and lack of valuable content • Emergence for strategies to promote de-lurking Bishop, J. (2007). Increasing participation in online communities: A framework for human–computer interaction. Computers in Human Behavior, 23(4), 1881–1893. Fan, Y.-W., Wu, C.-C., Chiang, L.-C. (2009). Knowledge sharing in virtual community: The comparison between contributors and lurkers. In Proc. Int. Conf. on Electronic Business, pp. 662–668. Lurking in OSNs: Principles, Models, and Methods Lurking and online behavioral models (1/2) Environmental factors that affect the user’s feeling and the user’s willingness to
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